Phylo Flash game helps trace genetic disease

Forget FarmVille and chuck Canabalt: this time-wasting Flash game will actually
do some good in the world as you idly click away.

Phylo, created
by bioinformaticians at Canada's McGill University, is a
pattern-matching puzzle game that will give researchers a better
insight into genetic codes, and will hopefully help identify the
origins of genetic disease. All you have to do is as match some
coloured blocks.

The squares represent the different letters of the genetic
code (A, C, G and T), and you're asked to best arrange two
different sequences of DNA, RNA or protein. You have to line up as many
same-coloured blocks as possible, while avoiding gaps (mutations),
to identify regions of similarity.

Similar regions across different genetic sequences are often
the result of shared evolutionary origins, and indicate traits that
are conserved across multiple species. That could be the colour of
an animal's eyes, or it could be heart disease or breast cancer. By
tracing the exact mutation point where these genetic diseases are
created, we could have more ammunition to fight against
them.

All the data goes to Santa
Cruz's genome browser at the University of California, which
catalogues billions of stretches of genetic information. Every
alignment received from the game is analysed and stored in the
database, and the newly optimised data will eventually be released
to researchers working in the field.

So why bother us busy humans with the task, and why not pass
the buck onto a supercomputer? Well, computers are notoriously rubbish at tasks
such as facial recognition and, in this case, pattern sorting. The
human brain can tackle simple pattern based problems far more
efficiently than a computer.

The same computer inadequacy lead to protein folding game Foldit, and supernova
hunting astronomer sim, Galaxy Zoo. Plus, the
crowdsourced science idea is pointedly reminiscent of Stanford's Folding@Home, a PC
screensaver and PS3 app that used distributed computing power to
tackle medical research while saving money on expensive
supercomputers.